Barcode processing - TF reporter library - generation 2
Tea Filipovska & Max Trauernicht
Netherlands Cancer Institute - van Steensel labm.trauernicht@nki.nl
09/07/2021
Aim
18,000 TF reporters were transfected into HepG2 and K562 cells (in total 6 different conditions), sequencing data yielded barcode counts of these experiments. These counts will be processed in this script.
Setup
Libraries
Functions
Loading data
Creating count data frames
Compare differently clustered pDNA data
Annotate data frame
Data quality plots
## barcode tf promoter
## 1246 TCCATCTAAGCG Trp53 mCMV
## 1768 CCACGATAGTAA Trp53 hBGm
## 2178 TGGTGAGCAATT Trp53 mCMV
## 2246 AGCAATAAGCCA Trp53 mCMV
## 3361 CGCCACAGTGTA Trp53 mCMV
## 3541 AACAGGCGGTCT Trp53 mCMV
## 3547 GCTCTCGAATTA Trp53 mCMV
## 3703 GTCTGTTCGATT Trp53 mCMV
## 3797 CTATTCTGTGCA Trp53 mCMV
## 4358 AACAGAACAAGG Trp53 mCMV
## 5109 TGGTGTAGCAGA Trp53 mCMV
## 5243 CAGCGACTGACA Trp53 mCMV
## 5305 CGATATAGCCAG Trp53 mCMV
## 5396 GCTCTACACGAG Trp53 mCMV
## 5655 CAAGGTATAGGA Trp53 mCMV
## 6927 TCGACCGGATTG Trp53 mCMV
## 7270 TCTCTCAGCGAA Trp53 hBGm
## 7359 ATCACTGTATCC Trp53 mCMV
## 7509 TGTAAGGTAGAG Trp53 mCMV
## 7883 TGGATTGAGACA Trp53 mCMV
## 8326 TCCGTAGCTTCC Trp53 mCMV
## 8358 TGTTCCTGTCTG Trp53 hBGm
## 9777 GAATAGACGGTG Trp53 mCMV
## 10215 CTTGACTCGCTG Trp53 mCMV
## 10606 TACAGCGTTACC Trp53 hBGm
## 10763 ATACTCGTTCGC Trp53 mCMV
## 11459 ACTCCGTATTGC Trp53 mCMV
## 11820 GTTCGAAGCTCA Trp53 mCMV
## 12698 CGTTCGGAGAGA Trp53 mCMV
## 12939 ACCTCTTAGGTC Trp53 mCMV
## 13232 CGTTGAATATCG Trp53 mCMV
## 13322 CGTGTACACAGC Trp53 mCMV
## 13349 CGTTGATACTCA Trp53 hBGm
## 13403 AGTCTACCTTCG Trp53 minP
## 13720 GGTCGCTCCATT Trp53 hBGm
## 13742 ACGCACGAGATT Trp53 mCMV
## 13887 CATTCCGCTTGT Trp53 minP
## 14349 TGGTGCACTGTC Trp53 mCMV
## 15447 ACACGTAGCTTA Trp53 mCMV
## 15666 CGTCCTGTAAGA Trp53 mCMV
## 15699 CGCGTCCATCAA Trp53 mCMV
## 15891 AAGCCTGACAAG Trp53 mCMV
## 16430 TCTCCTCTGTGG Trp53 mCMV
## 16926 CACCAAGGCTGA Trp53 mCMV
## 18859 TACGAACTGTGT Trp53 mCMV
## 18869 TCAGTCTACTCT Trp53 mCMV
## 19853 CACCGGAGATCT Trp53 mCMV
## 20265 ATAGAGGCTGAC Trp53 mCMV
## 20648 GGATCATAGTTC Trp53 mCMV
## 20737 GACTATCTAGCA Trp53 mCMV
## 21178 TCTACTGAGTGA Trp53 mCMV
## 21258 GAGCAGGATTGT Trp53 mCMV
## 21327 GCTGTGCAATCG Trp53 mCMV
## 22837 TGTCCGTAACTC Trp53 mCMV
## 23142 TGCTATTAGACC Trp53 mCMV
## 23540 AGTCTATTGGTG Trp53 mCMV
## 23913 CACGCGTCTGAT Trp53 mCMV
## 24108 CGGCCTCGTATT Trp53 hBGm
## 24967 CTACACACCGCT Trp53 mCMV
## 25028 AGGCGCTTAGGT Trp53 mCMV
## 25182 TGGACGCTCTTG Trp53 mCMV
## 25873 TCGTGTACTCCG Trp53 hBGm
## 26543 TCGCAAGTCAGT Trp53 mCMV
## 27779 CCACATCGGAGA Trp53 hBGm
Sample filtering
Normalization of barcode counts:
Divide cDNA barcode counts through pDNA barcode counts to get activity
Calculate mean activity - filter out outlier barcodes
Scaling data
Re-labeling rep 3 K562 sample
bc_df_cDNA_filt$sample[bc_df_cDNA_filt$sample == "R3_K562_DMSO"] <- "K562_stressed"
bc_df_cDNA_filt$condition[bc_df_cDNA_filt$sample == "K562_stressed"] <- "K562_stressed"Calculate correlations between technical replicates
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Correlation between replicates
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Session Info
paste("Run time: ",format(Sys.time()-StartTime))## [1] "Run time: 6.979267 mins"getwd()## [1] "/DATA/usr/m.trauernicht/projects/SuRE-TF/gen2"date()## [1] "Fri Jul 9 15:03:38 2021"sessionInfo()## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pheatmap_1.0.12 PCAtools_2.2.0
## [3] ggrepel_0.9.1 DESeq2_1.30.1
## [5] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [7] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
## [11] IRanges_2.24.1 S4Vectors_0.28.1
## [13] BiocGenerics_0.36.1 viridis_0.6.0
## [15] viridisLite_0.4.0 grr_0.9.5
## [17] tidyr_1.1.3 LncFinder_1.1.4
## [19] gridExtra_2.3 RColorBrewer_1.1-2
## [21] readr_1.4.0 haven_2.4.1
## [23] ggbeeswarm_0.6.0 plotly_4.9.3
## [25] tibble_3.1.1 dplyr_1.0.5
## [27] vwr_0.3.0 latticeExtra_0.6-29
## [29] lattice_0.20-41 stringdist_0.9.6.3
## [31] GGally_2.1.1 ggpubr_0.4.0
## [33] ggplot2_3.3.3 stringr_1.4.0
## [35] plyr_1.8.6 data.table_1.14.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1
## [3] lazyeval_0.2.2 splines_4.0.5
## [5] crosstalk_1.1.1 BiocParallel_1.24.1
## [7] digest_0.6.27 foreach_1.5.1
## [9] htmltools_0.5.1.1 fansi_0.4.2
## [11] memoise_2.0.0 magrittr_2.0.1
## [13] openxlsx_4.2.3 recipes_0.1.16
## [15] annotate_1.68.0 gower_0.2.2
## [17] prettyunits_1.1.1 jpeg_0.1-8.1
## [19] colorspace_2.0-0 blob_1.2.1
## [21] xfun_0.22 crayon_1.4.1
## [23] RCurl_1.98-1.3 jsonlite_1.7.2
## [25] genefilter_1.72.1 survival_3.2-10
## [27] iterators_1.0.13 glue_1.4.2
## [29] gtable_0.3.0 ipred_0.9-11
## [31] zlibbioc_1.36.0 XVector_0.30.0
## [33] seqinr_4.2-5 DelayedArray_0.16.3
## [35] BiocSingular_1.6.0 car_3.0-10
## [37] abind_1.4-5 scales_1.1.1
## [39] DBI_1.1.1 rstatix_0.7.0
## [41] Rcpp_1.0.6 xtable_1.8-4
## [43] progress_1.2.2 dqrng_0.3.0
## [45] rsvd_1.0.5 foreign_0.8-81
## [47] bit_4.0.4 proxy_0.4-25
## [49] lava_1.6.9 prodlim_2019.11.13
## [51] htmlwidgets_1.5.3 httr_1.4.2
## [53] ellipsis_0.3.2 farver_2.1.0
## [55] pkgconfig_2.0.3 reshape_0.8.8
## [57] XML_3.99-0.6 nnet_7.3-15
## [59] sass_0.3.1 locfit_1.5-9.4
## [61] utf8_1.2.1 caret_6.0-86
## [63] labeling_0.4.2 tidyselect_1.1.1
## [65] rlang_0.4.10 reshape2_1.4.4
## [67] AnnotationDbi_1.52.0 cachem_1.0.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_4.0.5 generics_0.1.0
## [73] RSQLite_2.2.7 ade4_1.7-16
## [75] broom_0.7.6 fastmap_1.1.0
## [77] evaluate_0.14 yaml_2.2.1
## [79] ModelMetrics_1.2.2.2 knitr_1.33
## [81] bit64_4.0.5 zip_2.1.1
## [83] purrr_0.3.4 sparseMatrixStats_1.2.1
## [85] nlme_3.1-152 compiler_4.0.5
## [87] beeswarm_0.3.1 curl_4.3
## [89] png_0.1-7 e1071_1.7-6
## [91] ggsignif_0.6.1 geneplotter_1.68.0
## [93] bslib_0.2.4 stringi_1.5.3
## [95] highr_0.9 forcats_0.5.1
## [97] Matrix_1.3-2 vctrs_0.3.8
## [99] pillar_1.6.0 lifecycle_1.0.0
## [101] jquerylib_0.1.4 irlba_2.3.3
## [103] cowplot_1.1.1 bitops_1.0-7
## [105] R6_2.5.0 rio_0.5.26
## [107] vipor_0.4.5 codetools_0.2-18
## [109] MASS_7.3-53.1 assertthat_0.2.1
## [111] withr_2.4.2 GenomeInfoDbData_1.2.4
## [113] mgcv_1.8-34 hms_1.0.0
## [115] beachmat_2.6.4 grid_4.0.5
## [117] rpart_4.1-15 timeDate_3043.102
## [119] class_7.3-18 DelayedMatrixStats_1.12.3
## [121] rmarkdown_2.7 carData_3.0-4
## [123] pROC_1.17.0.1 lubridate_1.7.10